ISBN: 9780137470358 - learning deep learning: theory and practice of neural networks, computer vision, natural language processing, and transformers using tensorflow (16 Ergebnisse)

Paperback. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New.

Zustand: New. In English.

Paperback. Zustand: Brand New. 576 pages. 9.06x7.36x1.02 inches. In Stock.

Zustand: New. 2021. 1st Edition. Paperback. . . . . . Books ship from the US and Ireland.

Zustand: New. &Uumlber den AutorMagnus Ekman, Ph.D., is a director of architecture at NVIDIA Corporation. His doctorate is in computer engineering, and he is the inventor of multiple patents. He was first exposed to artificial neural networks in the .

Paperback. Zustand: Brand New. 576 pages. 9.06x7.36x1.02 inches. In Stock.

paperback. Zustand: New.

Taschenbuch. Zustand: Neu. Neuware - NVIDIA's Full-Color Guide to Deep Learning: All StudentsNeed to Get Started and Get Results Learning Deep Learning is a complete guide to DL.Illuminating both the core concepts and the hands-on programming techniquesneeded to succeed, this book suits seasoned developers, data scientists,analysts, but also those with no prior machine learning or statisticsexperience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers,Magnus Ekman shows how to use them to build advanced architectures, includingthe Transformer. He describes how these concepts are used to build modernnetworks for computer vision and natural language processing (NLP), includingMask R-CNN, GPT, and BERT. And he explains how a natural language translatorand a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples usingTensorFlow with Keras. Corresponding PyTorch examples are provided online, andthe book thereby covers the two dominating Python libraries for DL used inindustry and academia. He concludes with an introduction to neural architecturesearch (NAS), exploring important ethical issues and providing resources forfurther learning. - Exploreand master core concepts: perceptrons, gradient-based learning, sigmoidneurons, and back propagation - See how DL frameworks make it easier to developmore complicated and useful neural networks - Discover how convolutional neuralnetworks (CNNs) revolutionize image classification and analysis - Apply recurrentneural networks (RNNs) and long short-term memory (LSTM) to text and othervariable-length sequences - Master NLP with sequence-to-sequence networks and theTransformer architecture - Build applications for natural language translation andimage captioning.…

Taschenbuch. Zustand: Neu. Neuware -NVIDIA's Full-Color Guide to Deep Learning: All StudentsNeed to Get Started and Get ResultsPearson, St.-Martin-Straße 82, 81541 München 752 pp. Englisch.

Taschenbuch. Zustand: Neu. Learning Deep Learning | Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow | Magnus Ekman | Taschenbuch | Kartoniert / Broschiert | Englisch | 2021 | Pearson | EAN 9780137470358 | Verantwortliche Person für die EU: Pearson, St.-Martin-Str. 82, 81541 München, salesde[at]pearson[dot]com | Anbieter: preigu.…

Taschenbuch. Zustand: Neu. Neuware -NVIDIA's Full-Color Guide to Deep Learning: All StudentsNeed to Get Started and Get Results Learning Deep Learning is a complete guide to DL.Illuminating both the core concepts and the hands-on programming techniquesneeded to succeed, this book suits seasoned developers, data scientists,analysts, but also those with no prior machine learning or statisticsexperience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers,Magnus Ekman shows how to use them to build advanced architectures, includingthe Transformer. He describes how these concepts are used to build modernnetworks for computer vision and natural language processing (NLP), includingMask R-CNN, GPT, and BERT. And he explains how a natural language translatorand a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples usingTensorFlow with Keras. Corresponding PyTorch examples are provided online, andthe book thereby covers the two dominating Python libraries for DL used inindustry and academia. He concludes with an introduction to neural architecturesearch (NAS), exploring important ethical issues and providing resources forfurther learning. - Exploreand master core concepts: perceptrons, gradient-based learning, sigmoidneurons, and back propagation - See how DL frameworks make it easier to developmore complicated and useful neural networks - Discover how convolutional neuralnetworks (CNNs) revolutionize image classification and analysis - Apply recurrentneural networks (RNNs) and long short-term memory (LSTM) to text and othervariable-length sequences - Master NLP with sequence-to-sequence networks and theTransformer architecture - Build applications for natural language translation andimage captioning 752 pp. Englisch.…